Barcodes have been utilized for identifying and pricing objects for more than thirty years. Most typically, barcodes are used in retail to identify the item of merchandise. For example, a gallon of milk may contain a barcode that, when scanned, will notify the cashier of the price of the milk.
Yet in recent years, barcodes have acquired new purposes as computers and barcode scanners have become more portable. The circuitry required to scan a conventional one-dimensional barcode can now be housed in a device as small as a typical keychain. As a result, many mobile telephones, personal digital assistants (“PDAs”), and pagers can be retrofitted with or connected to a laser-based scanning device. This allows the mobile device to function as a scanner capable of storing hundreds or thousands of scanned barcodes.
Currently, many cell phones and mobile devices are available with built-in cameras. The explosion of the availability of affordable digital cameras and their inclusion into mobile devices is driven by several factors. One of the most important is the recent availability of inexpensive image sensors based on CMOS technology. The cameras on these devices provide a means for capturing the barcode information which was previously only accessible via a laser-based scanner. Decoding barcode images from digital cameras included in mobile devices presents several difficult problems. These problems go well beyond the challenges addressed in commercial barcode readers. Some of these problems are addressed below:
Lighting:
Most mobile devices with integrated digital cameras do not have built-in flashes and rely solely on the ambient light for illumination. This can cause the image to be underexposed or overexposed depending upon the intensity of the ambient light. Bad lighting may also cause the image to appear shadowy.
Focus:
Digital cameras for portable devices are usually designed to work at a variety of distances. The need for a wider range of focus in cameras results in a trade off between the cost of the lens component and the sharpness of a typical image.
Low-Cost Lens Components:
In order to meet cost constraints of many portable device markets, manufacturers often compromise on the optical quality of camera lenses. This can present decoding technology with a different set of challenges from the simple focal length based focus problem noted above. Low-cost lens components can produce image distortions that are localized to a specific region or form a changing gradient across the image.
Limited Resolution:
The cost of a digital imaging CMOS sensor increases as the number of image pixels increases. Although the Asian market has seen the release of general purpose consumer devices like PDAs and cell phones with “megapixel” image resolution, the European and North American markets are now only seeing their emergence. Lower resolution images contain less detail and usually require further processing to be useful.
Based on the aforementioned described problems with mobile digital imaging, there clearly exists a need for an image enhancement algorithm which can compensate for the poor images which result from these shortcomings. Such an algorithm would allow a greater number of images acquired via digital imaging techniques to be decoded.